
We propose a GNN architecture where each node cluster contributing to the embedding is compliant to a specific constraint imposed by a given TK. The problem is framed as a node soft assignment task, where constraints are encoded as regularization terms within a plug-and-play pooling layer based on DiffPool [1]}.

We exploit a dual Lagrangian framework to learn the coefficients of the constraints, which are dynamically updated throughout the training phase, according to the degree of violations of each constraint.

We perform an extensive experimental evaluation on various datasets for the challenging task of sentence classification in argument mining.

